Falling is one of the growing problems of the elderly and is a common call to accident and emergency departments. Falls are a complex geriatric syndrome with various consequences ranging from mortality, morbidity, reduced functioning, and premature nursing home admissions. Around 40% of people aged 65 years and older fall annually, a rate which increases above 50% with advanced age and among people who live in residential care facilities or nursing homes. Even though falls do not always lead to injury, about 20% of those who fall need medical attention, 5% result in a fracture, and other serious injuries, including severe head injuries, joint distortions and dislocations. Soft-tissue bruises, contusions, and lacerations arise in 5 to 10% of cases. These percentages can be more than doubled for women aged 75 years or older. Fall-associated health care costs in the United States was estimated in 2001 as high as $500 million/year, a figure that does not assess the individual morbidity involved (disability, dependence, depression, unemployment, inactivity).
The need for strategies to reduce falls in the elderly is huge and growing, as the proportion of older people increases. As a consequence, technical solutions have been proposed for fall detection. This includes the use of smart cameras to localize people lying on the floor, smart canes to measure the sequence of events and gauge balance, Doppler radar, and multisensor combinations such as accelerometers and blood pressure and pressure sensors. While those solutions could help in detecting falls and raising alarms, they are only useful when falls have already occurred, with all the negative consequences. Strategies for preventing falls are urgently desired.
Unlike fall detection, automatic fall prediction is very challenging, in particular because of the difficulty of understanding the complex combination of intrinsic impairments and disabilities, and the environmental hazards that contribute to the causes. First, falls are multifactorial events that result from interactions between hazardous environment, hazards or hazardous activities and increased individual susceptibility from accumulated effects of age and disease. For instance, age-related changes in posture, muscle strength, and step height can impair a person's ability to avoid a fall after an unexpected trip or while reaching or bending. Accurate understanding of fall-related factors and their impact on the balance behavior of individuals is of importance. Secondly, tracking all related causes and deriving a clear picture require a viable distributed sensing infrastructure, as well as appropriate fusion methods to predict falls and alert patients, with the lowest possible false positive.
Published work in fall prevention is limited to off-line patient medication and environmental assessment. Real-time assessment of fall-related causes is addressed only in part in state-of-the-art research. For instance, smart carpets have been used to predict falls in elderly people with Alzheimer's. Other technologies make use of “smart shoes” to study the gait behavior of individuals. While these approaches may contribute to a fall prevention system, they fail to consider the intrinsic combination of all risk factors, particularly environmental hazards and configurations, to derive a more accurate picture of fall dangers for each individual.
More pragmatic solutions currently in use in hospitals and elderly care facilities are limited to protection around the bed with mats to limit the effect of falling. The more technical solutions use a pressure sensor above the mattress to detect patients leaving their beds and thereby alerting the caregivers. The main drawback of this solution is the high amount of blind notifications sent to the healthcare provider for every movement of the patient in and out of bed.
Frequent visits in patients' room just to figure out that the patient is sleeping are at best annoying. A viable solution would provide a global view of the patient space and patient position in the room so the caregiver can assess dangerous situations and decide on what action to take. In this regard, commercial solutions such as Careview use a camera to stream an image of the patients' room to the caregiver. Besides privacy concerns, caregivers have to constantly be watching the videos. With dozens of patients to monitor, this solution is not optimal.